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1 一般社団法人 電子情報通信学会 THE INSTITUTE OF ELECTRONICS, 社団法人 電子情報通信学会 INFORMATION AND COMMUNICATION ENGINEERS 信学技報 IEICE Technical Report NLC ( 信学技報 TECHNICAL REPORT OF IEICE. THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS 実テキストの情報分析のための頑健な言語処理基盤 河原 大輔 禎夫 黒橋 林部 祐太 森田 一 Arseny Tolmachev 京都大学 大学院情報学研究科 京都府京都市左京区吉田本町 あらまし ブログ tweet SNS などの実テキストには 物事や商品 サービスに関する評価 意見などの人々の生の 声が書かれており 人が意思決定をするとき あるいは企業が自社の商品の評価を分析する上で貴重な情報源となる これらの実テキストの分析を計算機で支援するためには 未知語や表記ゆれに対して頑健な形態素解析 構文解析な どの基盤解析が必要となる 本論文では 実テキストの情報分析のための頑健な言語処理基盤について議論する キーワード 実テキスト 形態素解析 構文解析 語彙知識 Robust Language Processing Infrastructure for Information Analysis of Real Texts Daisuke KAWAHARA, Sadao KUROHASHI, Yuta HAYASHIBE, Hajime MORITA, and Arseny TOLMACHEV Department of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto, Japan Abstract On real texts, such as blog articles, tweets and SNS texts, people s real voices are written about evaluations and opinions on things, products and services. These are valuable information sources when people make decisions, and companies evaluate their products. To automatically analyze such real texts, it is essential to robustly perform fundamental analyses, such as morphological analysis and syntactic parsing, because real texts contain many unknown words and spelling variations. This paper discusses a robust language processing infrastructure for information analysis of real texts. Key words real texts, morphological analysis, syntactic parsing, lexical knowledge ら役に立つ情報を得るためには 計算機で実テキストを解析 1. は じ め に 分析することが必要不可欠である 近年 インターネットが普及し 日本では人口の約 80%にあ 注 2 たる 1 億人が利用するようになっている その中で多くの 人が ブログ tweet SNS などに 物事や商品に対する評価 意見などを書き 情報発信している これらのテキスト (ここ では 実テキスト と呼ぶ は人々の生の声であるため 人が 意思決定をするとき あるいは企業が自社の商品の評価を分析 実テキストの解析 分析における問題として 新語 専門用 語 固有名詞などの未知語の問題 主にひらがな書きによる単 語区切りの曖昧性などがある 以下にこれらの例を示す (1 市ヶ谷有咲 の ねんどろいど 欲しいですねぇ (2 あの店は でもの がよく見つかる する上で貴重な情報源となる このような実テキストは ビッ (1 では 市ヶ谷有咲 という人名 (キャラクター名 および ね グデータ の一つであり 莫大な量が存在するため その中か んどろいど という語 (2 では でもの という語を正しく 認識する必要がある 注 1 第 3 著者の現在の所属はフェアリーデバイセズ株式会社 第 4 著者の現 在の所属は株式会社富士通研究所 注 2 総 務 省 平 成 28 年 版 情 報 通 信 白 書: johotsusintokei/whitepaper/ja/h28/html/nc html 我々は 実テキストの情報分析のための頑健な言語処理基盤 を開発しており 本稿ではこれまでの我々の取り組みについて 述べる 具体的には 形態素解析のための語彙獲得 形態素解 This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere. Copyright 2017 by IEICE
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3 2 JUMAN++ (NEWS WEB F FMAC-jpp FMAC-other NEWS WEB jpp other (NEWS+WEB NEWS 2,727 (36, (1,783 WEB 4,427 (13, (2,195 MeCab(+IPADIC JUMAN KyTea (Full SVM Model 4 JUMAN++V1 ( JUMAN++V2 ( CPU Intel i7-6850k 64GB OS Ubuntsu gcc Ofast -march=native ,000 5 JUMAN , unigram trigram unigram bigram RNN RNN RNN JUMAN++V2 3. JUMAN % SNS Fairy Morphological Annotated Corpus ( FMAC [6] 6 FMAC Wikipedia (3?? FMAC 2,000 1,400 7 FMAC-jpp: (741 FMAC-other: (644 JUMAN++ FMAC-jpp FMAC-other JUMAN++ JUMAN++ 1 JUMAN++ 2 (1? 3 (2 JUMAN++ NEWS [7] WEB [8] 3 NEWS WEB NEWS WEB NEWS WEB F FMAC-jpp FMAC-other 2 FMAC-jpp FMAC-other closed KyTea FMAC MeCab+Unidic (
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6 4 ( F KNP CaboCha KNP+CaboCha NEWS wo/ N-best N-best KNP CaboCha KNP+CaboCha WEB wo/ N-best N-best (10 JUMAN++ 4 (11 N-best 5. JUMAN Web [1] T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, Recurrent neural network based language model, Interspeech, vol.2, p.3, [2] H. Morita, D. Kawahara, and S. Kurohashi, Morphological analysis for unsegmented languages using recurrent neural network language model, Proceedings of EMNLP2015, pp , [3] 18 pp [4] Y. Murawaki and S. Kurohashi, Online acquisition of Japanese unknown morphemes using morphological constraints, Proceedings of EMNLP2008, pp , [5] J. Wang, P. Zhao, and S.C. Hoi, Exact soft confidenceweighted learning, Proceedings of ICML-12, pp , [6] 231 pp.9: [7] D. Kawahara, S. Kurohashi, and K. Hasida, Construction of a Japanese relevance-tagged corpus, Proceedings of LREC2002, pp , [8] vol.21 no.2 pp [9] 220 pp.1: [10] D. Kawahara, Y. Hayashibe, H. Morita, and S. Kurohashi, Automatically acquired lexical knowledge improves Japanese joint morphological and dependency analysis, Proceedings of IWPT2017, 2017 (to appear. [11] 224 pp.14: [12] T. Mikolov, C. Kai, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, Proceedings of Workshop at International Conference on Learning Representations, [13] vol.14 no.4 pp [14] vol.14 no.1 pp
実テキストの情報分析のための頑健な言語処理基盤
実テキストの情報分析のための頑健な言語処理基盤 河原大輔黒橋禎夫 * 林部祐太 *1 森田一 *2 Arseny Tolmachev 京都大学大学院情報学研究科 * 科学技術振興機構 CREST 1 現在の所属はフェアリーデバイセズ株式会社 2 現在の所属は株式会社富士通研究所 第 11 回テキストアナリティックス シンポジウム (2017/9/7) 実テキスト セブンの唐揚げ串美味しいし安いんですよね!!
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